From the course: End-to-End Data Engineering Project

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Reviewing and executing dbt

Reviewing and executing dbt

From the course: End-to-End Data Engineering Project

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Reviewing and executing dbt

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- [Instructor] Let's recap everything we have done with DVT and take a closer look at the SQL queries that form our models. In the previous lesson, we began by creating staging models in the staging directory. The purpose of the staging models is to pull our raw data and put it into a more workable format. The models, staging customers and staging orders are essentially selecting a subset of columns from our source tables, raw data customers, and raw data orders respectively. For example, in our staging customers model, we are not propagating the customer name and last name. This could be useful for privacy reasons. The staging models are also the right place to make small transformations, like casting. For example, we could cast the daytime columns here to timestamped if we would like to. You may have noticed that we are using the source function to refer to the source table. Using this function is better than directly…

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